A Retinomorphic Graphene Transistor Implementing Direction‐ and Velocity‐Selective Computation for In‐Sensor Image Stabilization

Neural circuits in the retina extract motion direction and velocity through asymmetric dendritic processing, but replicating these computations in solid-state hardware has not been achieved. Herein, a single multi-gate ion-gel graphene transistor (MIG-GFET) is demonstrated to emulate key operations of the direction-selective ganglion cell (DSGC) and starburst amacrine cell (SAC) microcircuit. Spatially distributed gates created artificial dendritic compartments whose ionic dynamics enabled nonlinear summation of sequential inputs, yielding intrinsic direction selectivity without algorithmic post-processing. An oppositely biased inhibitory gate reproduced SAC-like null-side inhibition and yielded a band-pass velocity response with a tunable preferred speed-closely mirroring the spatiotemporal filtering properties underlying direction- and speed-dependent firing in biological DSGCs. This velocity selectivity enables the MIG-GFET to function as an analog motion filter. By suppressing ego-motion-induced global scene jitter within a defined frequency band, the device achieves on-sensor image stabilization with lower latency and power consumption than digital post-processing. Therefore, dendritic-level computation can be embedded directly into a graphene transistor, offering a path toward vision hardware where sensing and early neural processing are co-localized at the device level. This compact, low-power approach is compatible with focal-plane array integration and broad applications in real-time motion analysis.

Authors

Institutions

Publication Details

Journal
Small
Published
2026-08-13
DOI
https://doi.org/10.1002/smll.75262
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

A Retinomorphic Graphene Transistor Implementing Direction‐ and Velocity‐Selective Computation for In‐Sensor Image Stabilization

HoYeon Kim, Byeong‐Kwon Ju, Hyun Jae Jang, K. S. Lee et al.
Small
Advanced Memory and Neural Computing
article

A Retinomorphic Graphene Transistor Implementing Direction‐ and Velocity‐Selective Computation for In‐Sensor Image Stabilization

HoYeon Kim, Byeong‐Kwon Ju, Hyun Jae Jang, K. S. Lee, Seongsik Park, Jongkil Park, Jae‐Wook Kim, Suyoun Lee, Inho Kim, Jun Ha Park, Gyu Weon Hwang, Jinkyung Kim
article en

Abstract

Neural circuits in the retina extract motion direction and velocity through asymmetric dendritic processing, but replicating these computations in solid-state hardware has not been achieved. Herein, a single multi-gate ion-gel graphene transistor (MIG-GFET) is demonstrated to emulate key operations of the direction-selective ganglion cell (DSGC) and starburst amacrine cell (SAC) microcircuit. Spatially distributed gates created artificial dendritic compartments whose ionic dynamics enabled nonlinear summation of sequential inputs, yielding intrinsic direction selectivity without algorithmic post-processing. An oppositely biased inhibitory gate reproduced SAC-like null-side inhibition and yielded a band-pass velocity response with a tunable preferred speed-closely mirroring the spatiotemporal filtering properties underlying direction- and speed-dependent firing in biological DSGCs. This velocity selectivity enables the MIG-GFET to function as an analog motion filter. By suppressing ego-motion-induced global scene jitter within a defined frequency band, the device achieves on-sensor image stabilization with lower latency and power consumption than digital post-processing. Therefore, dendritic-level computation can be embedded directly into a graphene transistor, offering a path toward vision hardware where sensing and early neural processing are co-localized at the device level. This compact, low-power approach is compatible with focal-plane array integration and broad applications in real-time motion analysis.

Small
Korea University (KR), Korea Institute of Science and Technology (KR)
Affordable and clean energy
Openalex Percentile: Top 18%
Advanced Memory and Neural Computing
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.